One way of handling model heterogeneities and independence in a federated learning setting is by using knowledge distillation [1]} with a common student model architecture on each local device [2]}. Label and model heterogeneities are handled in an inertial Human Activity Recognition scenario in [3]}. Federated learning for keyword spotting [4]}, and new class learning and identification in various speech recognition settings are addressed in [5]}, [6]}. [7]} proposes a new augmentation technique to reduce false reject rates and addresses algorithmic constraints in FL-KWS training to label examples with no visibility. However, the scope of our proposed work is different in the nature that it primarily addresses identification and similarity detection of new labels in a zero-shot manner when heterogeneous label and model distributions exist across various FL iterations and users. To the best of our knowledge, none of the papers discuss new label identification in FL settings with statistical heterogeneities for audio classification.
